You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
279 lines
13 KiB
279 lines
13 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
|
|
"""
|
|
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
|
Usage - sources:
|
|
$ yolo task=... mode=predict model=s.pt --source 0 # webcam
|
|
img.jpg # image
|
|
vid.mp4 # video
|
|
screen # screenshot
|
|
path/ # directory
|
|
list.txt # list of images
|
|
list.streams # list of streams
|
|
'path/*.jpg' # glob
|
|
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
|
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
|
Usage - formats:
|
|
$ yolo task=... mode=predict --weights yolov8n.pt # PyTorch
|
|
yolov8n.torchscript # TorchScript
|
|
yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
|
yolov8n_openvino_model # OpenVINO
|
|
yolov8n.engine # TensorRT
|
|
yolov8n.mlmodel # CoreML (macOS-only)
|
|
yolov8n_saved_model # TensorFlow SavedModel
|
|
yolov8n.pb # TensorFlow GraphDef
|
|
yolov8n.tflite # TensorFlow Lite
|
|
yolov8n_edgetpu.tflite # TensorFlow Edge TPU
|
|
yolov8n_paddle_model # PaddlePaddle
|
|
"""
|
|
import platform
|
|
from collections import defaultdict
|
|
from itertools import chain
|
|
from pathlib import Path
|
|
|
|
import cv2
|
|
|
|
from ultralytics.nn.autobackend import AutoBackend
|
|
from ultralytics.yolo.configs import get_config
|
|
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
|
|
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
|
|
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, SETTINGS, callbacks, colorstr, ops
|
|
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
|
|
from ultralytics.yolo.utils.files import increment_path
|
|
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
|
|
|
|
|
|
class BasePredictor:
|
|
"""
|
|
BasePredictor
|
|
|
|
A base class for creating predictors.
|
|
|
|
Attributes:
|
|
args (OmegaConf): Configuration for the predictor.
|
|
save_dir (Path): Directory to save results.
|
|
done_setup (bool): Whether the predictor has finished setup.
|
|
model (nn.Module): Model used for prediction.
|
|
data (dict): Data configuration.
|
|
device (torch.device): Device used for prediction.
|
|
dataset (Dataset): Dataset used for prediction.
|
|
vid_path (str): Path to video file.
|
|
vid_writer (cv2.VideoWriter): Video writer for saving video output.
|
|
annotator (Annotator): Annotator used for prediction.
|
|
data_path (str): Path to data.
|
|
"""
|
|
|
|
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
|
|
"""
|
|
Initializes the BasePredictor class.
|
|
|
|
Args:
|
|
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
|
|
overrides (dict, optional): Configuration overrides. Defaults to None.
|
|
"""
|
|
if overrides is None:
|
|
overrides = {}
|
|
self.args = get_config(config, overrides)
|
|
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
|
|
name = self.args.name or f"{self.args.mode}"
|
|
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
|
|
if self.args.save:
|
|
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
|
|
if self.args.conf is None:
|
|
self.args.conf = 0.25 # default conf=0.25
|
|
self.done_setup = False
|
|
|
|
# Usable if setup is done
|
|
self.model = None
|
|
self.data = self.args.data # data_dict
|
|
self.device = None
|
|
self.dataset = None
|
|
self.vid_path, self.vid_writer = None, None
|
|
self.annotator = None
|
|
self.data_path = None
|
|
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
|
|
callbacks.add_integration_callbacks(self)
|
|
|
|
def preprocess(self, img):
|
|
pass
|
|
|
|
def get_annotator(self, img):
|
|
raise NotImplementedError("get_annotator function needs to be implemented")
|
|
|
|
def write_results(self, results, batch, print_string):
|
|
raise NotImplementedError("print_results function needs to be implemented")
|
|
|
|
def postprocess(self, preds, img, orig_img):
|
|
return preds
|
|
|
|
def setup(self, source=None, model=None):
|
|
# source
|
|
source, webcam, screenshot, from_img = self.check_source(source)
|
|
# model
|
|
stride, pt = self.setup_model(model)
|
|
imgsz = check_imgsz(self.args.imgsz, stride=stride, min_dim=2) # check image size
|
|
|
|
# Dataloader
|
|
bs = 1 # batch_size
|
|
if webcam:
|
|
self.args.show = check_imshow(warn=True)
|
|
self.dataset = LoadStreams(source,
|
|
imgsz=imgsz,
|
|
stride=stride,
|
|
auto=pt,
|
|
transforms=getattr(self.model.model, 'transforms', None),
|
|
vid_stride=self.args.vid_stride)
|
|
bs = len(self.dataset)
|
|
elif screenshot:
|
|
self.dataset = LoadScreenshots(source,
|
|
imgsz=imgsz,
|
|
stride=stride,
|
|
auto=pt,
|
|
transforms=getattr(self.model.model, 'transforms', None))
|
|
elif from_img:
|
|
self.dataset = LoadPilAndNumpy(source,
|
|
imgsz=imgsz,
|
|
stride=stride,
|
|
auto=pt,
|
|
transforms=getattr(self.model.model, 'transforms', None))
|
|
else:
|
|
self.dataset = LoadImages(source,
|
|
imgsz=imgsz,
|
|
stride=stride,
|
|
auto=pt,
|
|
transforms=getattr(self.model.model, 'transforms', None),
|
|
vid_stride=self.args.vid_stride)
|
|
self.vid_path, self.vid_writer = [None] * bs, [None] * bs
|
|
self.model.warmup(imgsz=(1 if pt or self.model.triton else bs, 3, *imgsz)) # warmup
|
|
|
|
self.webcam = webcam
|
|
self.screenshot = screenshot
|
|
self.from_img = from_img
|
|
self.imgsz = imgsz
|
|
self.done_setup = True
|
|
return model
|
|
|
|
@smart_inference_mode()
|
|
def __call__(self, source=None, model=None, verbose=False, stream=False):
|
|
if stream:
|
|
return self.stream_inference(source, model, verbose)
|
|
else:
|
|
return list(chain(*list(self.stream_inference(source, model, verbose)))) # merge list of Result into one
|
|
|
|
def predict_cli(self):
|
|
# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
|
|
gen = self.stream_inference(verbose=True)
|
|
for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
|
|
pass
|
|
|
|
def stream_inference(self, source=None, model=None, verbose=False):
|
|
self.run_callbacks("on_predict_start")
|
|
if not self.done_setup:
|
|
self.setup(source, model)
|
|
self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
|
|
for batch in self.dataset:
|
|
self.run_callbacks("on_predict_batch_start")
|
|
path, im, im0s, vid_cap, s = batch
|
|
visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
|
|
with self.dt[0]:
|
|
im = self.preprocess(im)
|
|
if len(im.shape) == 3:
|
|
im = im[None] # expand for batch dim
|
|
|
|
# Inference
|
|
with self.dt[1]:
|
|
preds = self.model(im, augment=self.args.augment, visualize=visualize)
|
|
|
|
# postprocess
|
|
with self.dt[2]:
|
|
results = self.postprocess(preds, im, im0s)
|
|
for i in range(len(im)):
|
|
p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
|
|
p = Path(p)
|
|
|
|
if verbose or self.args.save or self.args.save_txt or self.args.show:
|
|
s += self.write_results(i, results, (p, im, im0))
|
|
|
|
if self.args.show:
|
|
self.show(p)
|
|
|
|
if self.args.save:
|
|
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
|
|
|
|
yield results
|
|
|
|
# Print time (inference-only)
|
|
if verbose:
|
|
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
|
|
|
|
self.run_callbacks("on_predict_batch_end")
|
|
|
|
# Print results
|
|
if verbose:
|
|
t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
|
|
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape '
|
|
f'{(1, 3, *self.imgsz)}' % t)
|
|
if self.args.save_txt or self.args.save:
|
|
s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels saved to {self.save_dir / 'labels'}" \
|
|
if self.args.save_txt else ''
|
|
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
|
|
|
|
self.run_callbacks("on_predict_end")
|
|
|
|
def setup_model(self, model):
|
|
device = select_device(self.args.device)
|
|
model = model or self.args.model
|
|
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
|
|
model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
|
|
self.model = model
|
|
self.device = device
|
|
self.model.eval()
|
|
return model.stride, model.pt
|
|
|
|
def check_source(self, source):
|
|
source = source if source is not None else self.args.source
|
|
webcam, screenshot, from_img = False, False, False
|
|
if isinstance(source, (str, int, Path)): # int for local usb carame
|
|
source = str(source)
|
|
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
|
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
|
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
|
|
screenshot = source.lower().startswith('screen')
|
|
if is_url and is_file:
|
|
source = check_file(source) # download
|
|
else:
|
|
from_img = True
|
|
return source, webcam, screenshot, from_img
|
|
|
|
def show(self, p):
|
|
im0 = self.annotator.result()
|
|
if platform.system() == 'Linux' and p not in self.windows:
|
|
self.windows.append(p)
|
|
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
|
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
|
cv2.imshow(str(p), im0)
|
|
cv2.waitKey(1) # 1 millisecond
|
|
|
|
def save_preds(self, vid_cap, idx, save_path):
|
|
im0 = self.annotator.result()
|
|
# save imgs
|
|
if self.dataset.mode == 'image':
|
|
cv2.imwrite(save_path, im0)
|
|
else: # 'video' or 'stream'
|
|
if self.vid_path[idx] != save_path: # new video
|
|
self.vid_path[idx] = save_path
|
|
if isinstance(self.vid_writer[idx], cv2.VideoWriter):
|
|
self.vid_writer[idx].release() # release previous video writer
|
|
if vid_cap: # video
|
|
fps = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec
|
|
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
|
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
|
else: # stream
|
|
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
|
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
|
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
|
self.vid_writer[idx].write(im0)
|
|
|
|
def run_callbacks(self, event: str):
|
|
for callback in self.callbacks.get(event, []):
|
|
callback(self)
|